Combining measurements based on beacon data

1. A method, comprising:

accessing, by a reporting server, site-centric data and panel-centric data;

segmenting, by the reporting server, the site-centric data and the panel-centric data, to determine initial usage measurement data for at least three types of client systems, the at least three types of client systems including a mobile system, shared use system, and non-shared use system;

determining, by the reporting server, one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data, the one or more adjustment factors including an identifier-per-person adjustment factor, a machine overlap adjustment factor, or a non-beaconed adjustment factor;

applying, by the reporting server, the one or more adjustment factors to the initial usage measurement data for the at least three types of client systems to generate an adjusted usage measurement data for the at least three types of client systems correcting inaccuracies of the initial usage measurement data; and

generating, by the reporting server, reports based on the adjusted usage measurement data for the at least three types of client systems.

2. The method of claim 1 , wherein:

the site-centric data are received from beacon instructions executed by content-presenting applications on client devices from which resource accesses originate,

the beacon instructions are configured to cause an executing client device to send a message to a collection server, and

the content-presenting applications on the client devices that execute the beacon instructions are otherwise unaffiliated with the collection server.

3. The method of claim 1 , wherein segmenting the site-centric data is further based on a time of day of access.

4. The method of claim 1 , wherein determining the one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data includes:

determining a number of unique visitors based on the panel-centric data;

determining a number of identifiers based on the panel-centric data; and

determining the identifier-per-person adjustment factor by taking a ratio of the number of identifiers to the number of unique visitors.

5. The method of claim 1 , wherein determining the one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data includes:

determining a client device-to-person ratio based on the panel-centric data;

determining an expected reach of panelists based on the client device-to-person ratio;

determining an incremental reach from additional client devices based on the client device-to-person ratio; and

determining the machine overlap adjustment factor by taking a ratio of the expected reach to the incremental reach.

6. The method of claim 1 , wherein determining the one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data includes:

determining a number of unique visitors for a webpage;

determining a number of members in the panel-centric data that visited the webpage and that sent a beacon message with a beacon identifier to determine a projection weight;

determining a number of page views for each of the members;

applying the projection weight to each of the numbers of page views to generate projected page views;

adding together the projected page views to obtain an overlap count of page views; and

determining the non-beaconed adjustment factor by subtracting the overlap count from the number of unique visitors.

7. The method of claim 1 , wherein the one or more adjustment factors comprise one or more overlap factors across the at least three types of client systems.

8. The method of claim 7 , wherein the one or more overlap factors comprise a usage overlap between non-shared client systems, shared client systems, and mobile devices based on the segmented site-centric data.

9. The method of claim 7 , further comprising:

determining, by the reporting server, a number of unique visitors for each of the at least three types of client systems;

adjusting, by the reporting server, the number of unique visitors by the one or more overlap factors; and

combining, by the reporting server, the adjusted number of unique visitors to produce a total count of unique visitors.

10. The method of claim 1 , further comprising accessing, by the reporting server, an estimated percentage of unique visitors on shared use client systems within a population being measured.

11. The method of claim 1 , further comprising determining one or more unique identifiers of the client systems based on at least one of a persistent cookie or a non-persistent cookie.

12. The method of claim 1 , further comprising determining one or more unique identifiers of the client systems based on at least one of a persistent IP address and a non-persistent IP address.

13. The method of claim 1 , further comprising:

determining, by the reporting server, a number of unique visitors based on the adjusted usage measurement data for the at least three types of client systems.

14. A non-transitory computer-readable medium storing instructions that, when executed by a processor, effectuate operations comprising:

accessing, by a reporting server, site-centric data and panel-centric data;

segmenting, by the reporting server, the site-centric data and the panel-centric data to determine initial usage measurement data for at least three types of client systems, the at least three types of client systems including a mobile system, shared use system, and non-shared use system;

determining, by the reporting server, one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data, the one or more adjustment factors including an identifier-per-person adjustment factor, a machine overlap adjustment factor, or a non-beaconed adjustment factor;

applying, by the reporting server, the one or more adjustment factors to the initial usage measurement data for the at least three types of client systems to generate an adjusted usage measurement data for the at least three types of client systems correcting inaccuracies of the initial usage measurement data; and

generating, by the reporting server, reports based on the adjusted usage measurement data for the at least three types of client systems.

15. The non-transitory computer-readable medium of claim 14 , wherein

the site-centric data are received from beacon instructions executed by content-presenting applications on client devices from which resource accesses originate,

the beacon instructions are configured to cause an executing client device to send a message to a collection server, and

the content-presenting applications on the client devices that execute the beacon instructions are otherwise unaffiliated with the collection server.

16. The non-transitory computer-readable medium of claim 14 , wherein segmenting the site-centric data is further based on a time of day of access.

17. The non-transitory computer-readable medium of claim 14 , wherein the instructions, when executed by the processor, effectuate further operations comprising:

determining, by the reporting server, a number of unique visitors based on the adjusted usage measurement data for the at least three types of client systems.

18. A device, comprising:

a processor; and

memory storing instructions that, when executed by the processor, effectuate operations comprising:

accessing, by a reporting server, site-centric data and panel-centric data;

segmenting, by the reporting server, the site-centric data and the panel-centric data to determine initial usage measurement data for at least three types of client systems the at least three types of client systems including a mobile system, shared use system, and non-shared use system;

determining, by the reporting server, one or more adjustment factors for each of the at least three types of client systems based on the segmented panel-centric data, the one or more adjustment factors including an identifier-per-person adjustment factor, a machine overlap adjustment factor, or a non-beaconed adjustment factor;

applying, by the reporting server, the one or more adjustment factors to the initial usage measurement data for the at least three types of client systems to generate an adjusted usage measurement data for the at least three types of client systems correcting inaccuracies of the initial usage measurement data; and

generating, by the reporting server, reports based on the adjusted usage measurement data for the at least three types of client systems.

19. The device of claim 18 , wherein:

the site-centric data are received from beacon instructions executed by content-presenting applications on client devices from which resource accesses originate,

the beacon instructions are configured to cause an executing client device to send a message to a collection server, and

the content-presenting applications on the client devices that execute the beacon instructions are otherwise unaffiliated with the collection server.

20. The device of claim 18 , wherein segmenting the site-centric data is further based on a time of day of access.

21. The device of claim 18 , wherein the instructions, when executed by the processor, effectuate further operations comprising:

determining, by the reporting server, a number of unique visitors based on the adjusted usage measurement data for the at least three types of client systems.